Mapping foreigner ratio to AfD election results in the German Wahlkreise

library(sf)
## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1
library(stringr)
library(tidyverse)
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## ✓ tibble  3.1.0     ✓ dplyr   1.0.5
## ✓ tidyr   1.1.3     ✓ forcats 0.5.1
## ✓ readr   1.4.0
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(magrittr)
## 
## Attaching package: 'magrittr'
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## 
##     set_names
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## 
##     extract
library(huxtable)
## 
## Attaching package: 'huxtable'
## The following object is masked from 'package:dplyr':
## 
##     add_rownames
## The following object is masked from 'package:ggplot2':
## 
##     theme_grey
library(broom)
library(viridis)
## Loading required package: viridisLite
wahlkreise_shp <- st_read(my_path_wahlkreise)
## Reading layer `Geometrie_Wahlkreise_20DBT' from data source 
##   `/Users/duzhiting/Documents/GitHub/R-Prediction/btw21_geometrie_wahlkreise_shp/Geometrie_Wahlkreise_20DBT.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 299 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 280371.1 ymin: 5235856 xmax: 921120.1 ymax: 6101444
## Projected CRS: ETRS89 / UTM zone 32N
glimpse(wahlkreise_shp)
## Rows: 299
## Columns: 5
## $ WKR_NR    <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 1…
## $ WKR_NAME  <chr> "Flensburg – Schleswig", "Nordfriesland – Dithmarschen Nord"…
## $ LAND_NR   <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", …
## $ LAND_NAME <chr> "Schleswig-Holstein", "Schleswig-Holstein", "Schleswig-Holst…
## $ geometry  <MULTIPOLYGON [m]> MULTIPOLYGON (((545529.8 60..., MULTIPOLYGON ((…
wahlkreise_shp %>%
  ggplot() +
  geom_sf()

# Foreign nationals ratios

foreign_file <- "~/Documents/Github/R-Prediction/btw21_Strukturdaten.csv"

file.exists(foreign_file)
## [1] TRUE
foreign_raw <- read_delim(foreign_file,
    ";", escape_double = FALSE,
    locale = locale(decimal_mark = ",",
        grouping_mark = "."),
    trim_ws = TRUE,
    skip = 8)  # skipt the first 8 rows
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   Land = col_character(),
##   `Wahlkreis-Nr.` = col_character(),
##   `Wahlkreis-Name` = col_character(),
##   `Fläche am 31.12.2019 (km²)` = col_number(),
##   Fußnoten = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

we need to do some cleansing before we can work with this dataset.

foreign_names <- names(foreign_raw)

foreign_df <- foreign_raw

names(foreign_df) <- paste0("V",1:ncol(foreign_df))

The important columns are:

foreign_df <- foreign_df %>% 
  rename(state = V1,
         area_nr = V2,
         area_name = V3,
         for_prop = V8,
         pop_move = V11,
         pop_migr_background = V19,
         income = V26,
         unemp = V47)  # total, as to March 2017

AfD election results

elec_results = read.csv2("~/Documents/Github/R-Prediction/kerg.csv", head = TRUE, sep="\t")
head(elec_results)
NrGebietWaehler_gueltige_Zweitstimmen_vorlauefigAfD3
1Flensburg – Schleswig18011210317
2Nordfriesland – Dithmarschen Nord1453878798
3Steinburg – Dithmarschen Süd13629911303
4Rendsburg-Eckernförde16206010564
5Kiel1559867654
6Plön – Neumünster1337219741
afd_prop <- elec_results %>% 

  rename(afd_votes = AfD3,
         area_nr = Nr,
         area_name = Gebiet,
         votes_total = Waehler_gueltige_Zweitstimmen_vorlauefig) %>% 
  mutate(afd_prop = afd_votes / votes_total) %>% 
  na.omit

Match foreign national rated to AfD votes for each Wahlkreis

foreign_df$area_nr <- as.integer(foreign_df$area_nr)
wahlkreise_shp %>% 
  left_join(foreign_df, by = c("WKR_NR" = "area_nr")) %>% 
  left_join(afd_prop, by = "area_name") -> chloro_data
chloro_data %>% 
  ggplot() +
  geom_sf(aes(fill = afd_prop)) -> p1
p1

p1 + scale_fill_distiller(palette = "Spectral") +
  theme_void()

# Geo map (of election areas) with foreign national data

chloro_data %>% 
  ggplot() +
  geom_sf(aes(fill = for_prop)) +
  scale_fill_distiller(palette = "Spectral") +
  theme_void() -> p2
p2

# “AfD to foreigner density”

chloro_data %>% 
  mutate(afd_for_dens = afd_prop / (for_prop/100)) -> chloro_data
  
chloro_data %>% 
  ggplot +
  geom_sf(aes(fill = afd_for_dens)) +
  theme_void() +
  scale_fill_viridis()

chloro_data %>% 
  select(afd_for_dens, afd_prop, for_prop) %>% 
  as.data.frame %>% 
  slice(1:3)

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6004708.9066, 6005277.6041, 6005951.0967, 6006006.9705, 6006362.3337, 6006445.5359, 6006465.0517, 6006581.0126, 6006705.6636, 6005869.8478, 6005222.7225)))
The diagram shows that in relation to foreigner rates, the AfD votes are strongest in Saxonian Wahlkreise primarily. Second, the East is surprisingly strong more “AfD dense” compared to the West. Don’t forget that this measure is an indication of co-occurrence, not of absolute AfD votes.

Correlation of foreign national quote and AfD votes

chloro_data %>% 
  select(for_prop, afd_prop, area_name) %>% 
  ggplot +
  aes(x = for_prop, y = afd_prop) +
  geom_point() +
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).

The correlation itself is

chloro_data %>% 
  select(for_prop, afd_prop, area_name) %>% 
  as.data.frame %T>% 
  summarise(cor_afd_foreigners = cor(afd_prop, for_prop)) %>% 
  do(tidy(cor.test(.$afd_prop, .$for_prop)))
estimatestatisticp.valueparameterconf.lowconf.highmethodalternative
-0.572-123.6e-27295-0.644-0.49Pearson's product-moment correlationtwo.sided

Regression residuals of predicting foreigner quote by afd_score

Let’s predict the AfD vote score taking the unemployment as an predictor. Then let’s plot the residuals to see how good the prediction is, ie., how close (or rather, far) the association of unemployment and AfD voting is

chloro_data$afd_prop[is.na(chloro_data$afd_prop)] <- 0
lm2 <- lm(afd_prop ~ for_prop, data = chloro_data)

glance(lm2)
r.squaredadj.r.squaredsigmastatisticp.valuedflogLikAICBICdeviancedf.residualnobs
0.310.3070.04881331.03e-251480-954-9430.706297299
tidy(lm2)
termestimatestd.errorstatisticp.value
(Intercept)0.172  0.0065226.31.47e-79
for_prop-0.005660.00049-11.51.03e-25
chloro_data %>% 
  mutate(afd_lm2 = lm(afd_prop ~ for_prop, data = .)$residuals) -> chloro_data
chloro_data %>% 
  select(afd_lm2) %>% 
  ggplot() +
  geom_sf(aes(fill = afd_lm2)) +
  scale_fill_gradient2() +
  theme_void()

Let’s include east as a predictor to the linear model:

lm3 <- lm(afd_prop ~ for_prop*east, data = chloro_data)

glance(lm3)
r.squaredadj.r.squaredsigmastatisticp.valuedflogLikAICBICdeviancedf.residualnobs
0.7660.7630.02853221.25e-923642-1.27e+03-1.25e+030.239295299
tidy(lm3)
termestimatestd.errorstatisticp.value
(Intercept)0.0948  0.00499 19   3.63e-53
for_prop-0.0009610.000347-2.770.00599 
eastTRUE0.199   0.0117  17   8.38e-46
for_prop:eastTRUE-0.015   0.00201 -7.488.46e-13

R squared increased dramatically, fostering the line of thought in the EDIT above. Now, we see that the general foreigner quote is not significiant anymore; we may infer that it plays no important role. But whether a wahlrkeis is East or not does play a strong role. For the East, the slope decreases quite a bit indicating some negative effect on foreigner quotes to AfD success.